astronomer/agents/cosmos-dbt-fusion

Name: cosmos-dbt-fusion
Author: astronomer

Claude Code and agent skill by astronomer/agents

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Run a dbt Fusion project with Astronomer Cosmos. Use when running a dbt Fusion project with Astronomer Cosmos (Cosmos 1.11+, ExecutionMode.LOCAL on Snowflake/Databricks). Before implementing, verify dbt engine is Fusion (not Core), the warehouse is supported, and local execution is acceptable. Does not cover dbt Core.

Install

npx vskill@latest install astronomer/agents/cosmos-dbt-fusion

SKILL.md

Cosmos + dbt Fusion: Implementation Checklist

Execute steps in order. This skill covers Fusion-specific constraints only.

Version note: dbt Fusion support was introduced in Cosmos 1.11.0. Requires Cosmos ≥1.11.
Reference: See [reference/cosmos-config.md](reference/cosmos-config.md) for ProfileConfig, operator_args, and Airflow 3 compatibility details.

Before starting, confirm: (1) dbt engine = Fusion (not Core → use cosmos-dbt-core), (2) warehouse = Snowflake, Databricks, Bigquery and Redshift only.

Fusion-Specific Constraints

Constraint	Details
No async	`AIRFLOW_ASYNC` not supported
No virtualenv	Fusion is a binary, not a Python package
Warehouse support	Snowflake, Databricks, Bigquery and Redshift support while in preview

1. Confirm Cosmos Version

CRITICAL: Cosmos 1.11.0 introduced dbt Fusion compatibility.

bash

# Check installed version
pip show astronomer-cosmos
 
# Install/upgrade if needed
pip install "astronomer-cosmos>=1.11.0"

Validate: pip show astronomer-cosmos reports version ≥ 1.11.0

2. Install the dbt Fusion Binary (REQUIRED)

dbt Fusion is NOT bundled with Cosmos or dbt Core. Install it into the Airflow runtime/image.

Determine where to install the Fusion binary (Dockerfile / base image / runtime).

Example Dockerfile Install

dockerfile

USER root
RUN apt-get update && apt-get install -y curl
ENV SHELL=/bin/bash
RUN curl -fsSL https://public.cdn.getdbt.com/fs/install/install.sh | sh -s -- --update
USER astro

Common Install Paths

Environment	Typical path
Astro Runtime	`/home/astro/.local/bin/dbt`
System-wide	`/usr/local/bin/dbt`

Validate: The dbt binary exists at the chosen path and dbt --version succeeds.

3. Choose Parsing Strategy (RenderConfig)

Parsing strategy is the same as dbt Core. Pick ONE:

Load mode	When to use	Required inputs
`dbt_manifest`	Large projects; fastest parsing	`ProjectConfig.manifest_path`
`dbt_ls`	Complex selectors; need dbt-native selection	Fusion binary accessible to scheduler
`automatic`	Simple setups; let Cosmos pick	(none)

python

from cosmos import RenderConfig, LoadMode
_render_config = RenderConfig(
    load_method=LoadMode.AUTOMATIC,  # or DBT_MANIFEST, DBT_LS
)

4. Configure Warehouse Connection (ProfileConfig)

Reference: See [reference/cosmos-config.md](reference/cosmos-config.md#profileconfig-warehouse-connection) for full ProfileConfig options and examples.

python

from cosmos import ProfileConfig
from cosmos.profiles import SnowflakeUserPasswordProfileMapping
_profile_config = ProfileConfig(
    profile_name="default",
    target_name="dev",
    profile_mapping=SnowflakeUserPasswordProfileMapping(
        conn_id="snowflake_default",
    ),
)

5. Configure ExecutionConfig (LOCAL Only)

CRITICAL: dbt Fusion with Cosmos requires ExecutionMode.LOCAL with dbt_executable_path pointing to the Fusion binary.

python

from cosmos import ExecutionConfig
from cosmos.constants import InvocationMode
_execution_config = ExecutionConfig(
    invocation_mode=InvocationMode.SUBPROCESS,
    dbt_executable_path="/home/astro/.local/bin/dbt",  # REQUIRED: path to Fusion binary
    # execution_mode is LOCAL by default - do not change
)

6. Configure Project (ProjectConfig)

python

from cosmos import ProjectConfig
_project_config = ProjectConfig(
    dbt_project_path="/path/to/dbt/project",
    # manifest_path="/path/to/manifest.json",  # for dbt_manifest load mode
    # install_dbt_deps=False,  # if deps precomputed in CI
)

7. Assemble DAG / TaskGroup

Option A: DbtDag (Standalone)

python

from cosmos import DbtDag, ProjectConfig, ProfileConfig, ExecutionConfig, RenderConfig
from cosmos.profiles import SnowflakeUserPasswordProfileMapping
from pendulum import datetime
_project_config = ProjectConfig(
    dbt_project_path="/usr/local/airflow/dbt/my_project",
)
_profile_config = ProfileConfig(
    profile_name="default",
    target_name="dev",
    profile_mapping=SnowflakeUserPasswordProfileMapping(
        conn_id="snowflake_default",
    ),
)
_execution_config = ExecutionConfig(
    dbt_executable_path="/home/astro/.local/bin/dbt",  # Fusion binary
)
_render_config = RenderConfig()
my_fusion_dag = DbtDag(
    dag_id="my_fusion_cosmos_dag",
    project_config=_project_config,
    profile_config=_profile_config,
    execution_config=_execution_config,
    render_config=_render_config,
    start_date=datetime(2025, 1, 1),
    schedule="@daily",
)

Option B: DbtTaskGroup (Inside Existing DAG)

python

from airflow.sdk import dag, task  # Airflow 3.x
# from airflow.decorators import dag, task  # Airflow 2.x
from airflow.models.baseoperator import chain
from cosmos import DbtTaskGroup, ProjectConfig, ProfileConfig, ExecutionConfig
from pendulum import datetime
_project_config = ProjectConfig(dbt_project_path="/usr/local/airflow/dbt/my_project")
_profile_config = ProfileConfig(profile_name="default", target_name="dev")
_execution_config = ExecutionConfig(dbt_executable_path="/home/astro/.local/bin/dbt")
@dag(start_date=datetime(2025, 1, 1), schedule="@daily")
def my_dag():
    @task
    def pre_dbt():
        return "some_value"
    dbt = DbtTaskGroup(
        group_id="dbt_fusion_project",
        project_config=_project_config,
        profile_config=_profile_config,
        execution_config=_execution_config,
    )
    @task
    def post_dbt():
        pass
    chain(pre_dbt(), dbt, post_dbt())
my_dag()

8. Final Validation

Before finalizing, verify:

[ ] Cosmos version: ≥1.11.0
[ ] Fusion binary installed: Path exists and is executable
[ ] Warehouse supported: Snowflake, Databricks, Bigquery or Redshift only
[ ] Secrets handling: Airflow connections or env vars, NOT plaintext

Troubleshooting

If user reports dbt Core regressions after enabling Fusion:

bash

AIRFLOW__COSMOS__PRE_DBT_FUSION=1

User Must Test

[ ] The DAG parses in the Airflow UI (no import/parse-time errors)
[ ] A manual run succeeds against the target warehouse (at least one model)

Reference

Cosmos dbt Fusion docs: https://astronomer.github.io/astronomer-cosmos/configuration/dbt-fusion.html
dbt Fusion install: https://docs.getdbt.com/docs/core/pip-install#dbt-fusion

Related Skills

cosmos-dbt-core: For dbt Core projects (not Fusion)
authoring-dags: General DAG authoring patterns
testing-dags: Testing DAGs after creation

Security

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Source

SKILL.md in the repositoryCheckingskills/cosmos-dbt-fusion/SKILL.md

Popularity

Repository stars: 312
Repository forks: 37
7-day trend score: 29

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 13 Apr 2026, 6 months ago.

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